Berita BPI LIPI
oleh : , Keum-Shik Hong ; Shuzhi Sam Ge
EEG recordings provide an important means of brain-computer communication, but their classification accuracy is limited by unforeseeable signal variations due to artifacts or recognizer-subject feedback. A number of techniques recently have been developed to address the related problem of recognizer robustness to uncontrollable signal variation. In this paper, we propose a classification method entailing time-series EEG signals with backpropagation neural networks (BPNN). To test the improvement in the EEG classification performance with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis (BLDA).